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⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit

Home Page: https://www.paddlepaddle.org.cn/fastdeploy

License: Apache License 2.0

Shell 1.16% C++ 74.79% Python 16.86% C 0.42% CMake 6.29% Batchfile 0.47%

fastdeploy's Introduction

English | 简体中文

⚡️FastDeploy

⚡️FastDeploy is an accessible and efficient deployment Development Toolkit. It covers 🔥critical AI models in the industry and provides 📦out-of-the-box deployment experience. It covers image classification, object detection, image segmentation, face detection, face recognition, human keypoint detection, OCR, semantic understanding and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform .

Potrait Segmentation Image Matting Semantic Segmentation Real-Time Matting
OCR Behavior Recognition Object Detection Pose Estimation
Face Alignment 3D Object Detection Face Editing Image Animation

Updates

  • 🔥 2022.8.18:Release FastDeploy release/v0.2.0
    • New server-side deployment upgrade: faster inference performance, support more vision model
      • Release high-performance inference engine SDK based on x86 CPUs and NVIDIA GPUs, with significant increase in inference speed
      • Integrate Paddle Inference, ONNXRuntime, TensorRT and other inference engines and provide a seamless deployment experience
      • Supports full range of object detection models such as YOLOv7, YOLOv6, YOLOv5, PP-YOLOE and provides End-To-End Deployment Demos
      • Support over 40 key models and Demo Examples including face detection, face recognition, real-time portrait matting, image segmentation.
      • Support deployment in both Python and C++
    • Supports Rockchip, Amlogic, NXP and other NPU chip deployment capabilities on edge device deployment

Contents

Data Center and Cloud Deployment

A Quick Start for Python SDK

Installation

Prerequisites
  • CUDA >= 11.2
  • cuDNN >= 8.0
  • python >= 3.6
  • OS: Linux x86_64/macOS/Windows 10
Install Library with GPU Support
pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
Install CPU-only Library
pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

Python Inference Example

  • Prepare models and pictures
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
  • Test inference results
# For deployment of GPU/TensorRT, please refer to examples/vision/detection/paddledetection/python
import cv2
import fastdeploy.vision as vision
  

model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                 "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                 "ppyoloe_crn_l_300e_coco/infer_cfg.yml")
im = cv2.imread("000000014439.jpg")
result = model.predict(im.copy())
print(result)

vis_im = vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("vis_image.jpg", vis_im)

A Quick Start for C++ SDK

Installation

C++ Inference Example

  • Prepare models and pictures
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
  • Test inference results
// For GPU/TensorRT deployment, please refer to examples/vision/detection/paddledetection/cpp
#include "fastdeploy/vision.h"

int main(int argc, char* argv[]) {
  namespace vision = fastdeploy::vision;
  auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                          "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                          "ppyoloe_crn_l_300e_coco/infer_cfg.yml");
  auto im = cv::imread("000000014439.jpg");

  vision::DetectionResult res;
  model.Predict(&im, &res)

  auto vis_im = vision::Visualize::VisDetection(im, res, 0.5);
  cv::imwrite("vis_image.jpg", vis_im);
 }

For more deployment models, please refer to Vision Model Deployment Examples .

Supported Server-Side Model List🔥🔥🔥

Notes:

(1) ✅: already supported; (2) ❔: to be supported in the future; (3) ❌: not supported at the moment; (4) --: not considered at the moment;
Hyperlinks:Click model's name, the website will jump to the model inference demo code

Task Model API Linux Linux Win Win Mac Mac Linux Linux
--- --- --- X86 CPU NVIDIA GPU Intel CPU NVIDIA GPU Intel CPU Arm CPU AArch64 CPU NVIDIA Jetson
Classification PaddleClas/ResNet50 Python/C++
Classification PaddleClas/PP-LCNet Python/C++
Classification PaddleClas/PP-LCNetv2 Python/C++
Classification PaddleClas/EfficientNet Python/C++
Classification PaddleClas/GhostNet Python/C++
Classification PaddleClas/MobileNetV1 Python/C++
Classification PaddleClas/MobileNetV2 Python/C++
Classification PaddleClas/MobileNetV3 Python/C++
Classification PaddleClas/ShuffleNetV2 Python/C++
Classification PaddleClas/SqueeezeNetV1.1 Python/C++
Classification PaddleClas/Inceptionv3 Python/C++
Classification PaddleClas/PP-HGNet Python/C++
Classification PaddleClas/SwinTransformer Python/C++
Detection PaddleDetection/PP-YOLOE Python/C++
Detection PaddleDetection/PicoDet Python/C++
Detection PaddleDetection/YOLOX Python/C++
Detection PaddleDetection/YOLOv3 Python/C++
Detection PaddleDetection/PP-YOLO Python/C++
Detection PaddleDetection/PP-YOLOv2 Python/C++
Detection PaddleDetection/FasterRCNN Python/C++
Detection Megvii-BaseDetection/YOLOX Python/C++
Detection WongKinYiu/YOLOv7 Python/C++
Detection meituan/YOLOv6 Python/C++
Detection ultralytics/YOLOv5 Python/C++
Detection WongKinYiu/YOLOR Python/C++
Detection WongKinYiu/ScaledYOLOv4 Python/C++
Detection ppogg/YOLOv5Lite Python/C++
Detection RangiLyu/NanoDetPlus Python/C++
Segmentation PaddleSeg/PP-LiteSeg Python/C++
Segmentation PaddleSeg/PP-HumanSegLite Python/C++
Segmentation PaddleSeg/HRNet Python/C++
Segmentation PaddleSeg/PP-HumanSegServer Python/C++
Segmentation PaddleSeg/Unet Python/C++
Segmentation PaddleSeg/Deeplabv3 Python/C++
FaceDetection biubug6/RetinaFace Python/C++
FaceDetection Linzaer/UltraFace Python/C++
FaceDetection deepcam-cn/YOLOv5Face Python/C++
FaceDetection deepinsight/SCRFD Python/C++
FaceRecognition deepinsight/ArcFace Python/C++
FaceRecognition deepinsight/CosFace Python/C++
FaceRecognition deepinsight/PartialFC Python/C++
FaceRecognition deepinsight/VPL Python/C++
Matting ZHKKKe/MODNet Python/C++

Edge-Side Deployment

EasyEdge Edge-Side Deployment

EasyEdge Deployment on Mobile Devices

EasyEdge Customized Deployment

Paddle Lite NPU Deployment

Supported Edge-Side Model List

Model Size (MB) Linux Android iOS Linux Linux Linux TBD...
--- --- --- ARM CPU ARM CPU ARM CPU Rockchip-NPU
RV1109
RV1126
RK1808
Amlogic-NPU
A311D
S905D
C308X
NXPNPU
i.MX 8M Plus
TBD...|
Classification PP-LCNet 11.9 -- -- -- --
Classification PP-LCNetv2 26.6 -- -- -- --
Classification EfficientNet 31.4 -- -- -- --
Classification GhostNet 20.8 -- -- -- --
Classification MobileNetV1 17 -- -- -- --
Classification MobileNetV2 14.2 -- -- -- --
Classification MobileNetV3 22
Classification ShuffleNetV2 9.2 -- -- -- --
Classification SqueezeNetV1.1 5
Classification Inceptionv3 95.5 -- -- -- --
Classification PP-HGNet 59 -- -- -- --
Classification SwinTransformer_224_win7 352.7 -- -- -- --
Detection PP-PicoDet_s_320_coco 4.1 -- -- -- --
Detection PP-PicoDet_s_320_lcnet 4.9
Detection CenterNet 4.8 -- -- -- --
Detection YOLOv3_MobileNetV3 94.6 -- -- -- --
Detection PP-YOLO_tiny_650e_coco 4.4 -- -- -- --
Detection SSD_MobileNetV1_300_120e_voc 23.3 -- -- -- --
Detection PP-YOLO_ResNet50vd 188.5 -- -- -- --
Detection PP-YOLOv2_ResNet50vd 218.7 -- -- -- --
Detection PP-YOLO_crn_l_300e_coco 209.1 -- -- -- --
Detection YOLOv5s 29.3 -- -- -- --
Face Detection BlazeFace 1.5 -- -- -- --
Face Detection RetinaFace 1.7 -- -- -- --
Keypoint Detection PP-TinyPose 5.5
Segmentation PP-LiteSeg(STDC1) 32.2 -- -- -- --
Segmentation PP-HumanSeg-Lite 0.556 -- -- -- --
Segmentation HRNet-w18 38.7 -- -- -- --
Segmentation PP-HumanSeg-Server 107.2 -- -- -- --
Segmentation Unet 53.7 -- -- -- --
OCR PP-OCRv1 2.3+4.4 -- -- -- --
OCR PP-OCRv2 2.3+4.4 -- -- -- --
OCR PP-OCRv3 2.4+10.6
OCR PP-OCRv3-tiny 2.4+10.7 -- -- -- --

Community

  • If you have any question or suggestion, please give us your valuable input via GitHub Issues
  • Join Us👬: Scan the QR code via WeChat to join our FastDeploy technology communication group

Acknowledge

We sincerely appreciate the open-sourced capabilities in EasyEdge as we adopt it for the SDK generation and download in this project.

License

FastDeploy is provided under the Apache-2.0.

fastdeploy's People

Contributors

chaoii avatar d-danielyang avatar deftruth avatar felixhjh avatar heliqi avatar jiangjiajun avatar joey12300 avatar leiqing1 avatar onecatcn avatar wjj19950828 avatar yunyaoxyy avatar zeyuchen avatar ziqi-jin avatar

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